Descriptive checks

Here we provide a descriptive overview of the syllable identifications relative to target (table 1). In the current data the number of syllables identified by EasyAlign perfectly matched the targeted number of syllables, i.e., in 100% of the trials there were 0 differences in the number of syllable detected versus target.

Table 1. Percentage of syllable detection mismatches (#tab:table01)
A summary table of percentage of differences between syllables
syllable differences percentage
0 100.00

Table 2 provides the percentages of different type of L2 stress placement matches and mismatches.

Table 2. Percentage of correct L2 placements (#tab:unnamed-chunk-1)
A summary table of percentage of stress match/mismatch types
stress mis/match type percentage
L2 match & L1 match 33.93
L2 match & L1 mismatch 39.88
L2 mismatch & L1 match 5.65
L2 mismatch & L1 mismatch 20.54

Main Confirmatory Analysis

Gesture vs. no gesture

For the first analysis we simply assess whether the absolute difference in stress timing relative to the target stress time is different for the gesture or the no gesture condition.

D$accuracy <- abs(D$stressed_mistimingL2L1) #absolute deviation from stress from L2

#basemodel predicting the overall mean accuracy
model0 <- lme(accuracy~1, data = D, random = list(~1|ppn, ~1|target), method = "ML", na.action = na.exclude)

#alternative model with gesture versus no gesture as predictor
model1 <- lme(accuracy~condition, data = D, random =  list(~1|ppn, ~1|target), method = "ML", na.action = na.exclude)
anova(model0, model1) #test difference basemodel versus model 1
##        Model df      AIC      BIC    logLik   Test  L.Ratio p-value
## model0     1  4 4069.241 4084.509 -2030.620                        
## model1     2  5 4066.132 4085.217 -2028.066 1 vs 2 5.109432  0.0238
summary(model1) 
## Linear mixed-effects model fit by maximum likelihood
##  Data: D 
##        AIC      BIC    logLik
##   4066.132 4085.217 -2028.066
## 
## Random effects:
##  Formula: ~1 | ppn
##         (Intercept)
## StdDev:    0.011073
## 
##  Formula: ~1 | target %in% ppn
##         (Intercept) Residual
## StdDev:     58.9668 85.65074
## 
## Fixed effects: accuracy ~ condition 
##                       Value Std.Error  DF  t-value p-value
## (Intercept)        40.91071  8.046698 167 5.084162  0.0000
## conditionnogesture 21.28571  9.373199 167 2.270912  0.0244
##  Correlation: 
##                    (Intr)
## conditionnogesture -0.582
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7144678 -0.4332536 -0.1847360 -0.1847359  4.1253449 
## 
## Number of Observations: 336
## Number of Groups: 
##             ppn target %in% ppn 
##               2             168

L1 L2 competition and asynchrony

For the second main analysis we assess only trials which indicate

#basemodel predicting the overall mean accuracy
model0 <- lme(asynchrony_L2L1~1, data = subD, random = list(~1|ppn, ~1|target), method = "ML", na.action = na.exclude)

#alternative model with gesture versus no gesture as predictor
model1 <- lme(asynchrony_L2L1~stress, data = subD, random =  list(~1|ppn, ~1|target), method = "ML", na.action = na.exclude)
anova(model0, model1) #test difference basemodel versus model 1
##        Model df      AIC      BIC    logLik   Test    L.Ratio p-value
## model0     1  4 2133.622 2146.118 -1062.811                          
## model1     2  5 2135.608 2151.228 -1062.804 1 vs 2 0.01353714  0.9074
summary(model1) 
## Linear mixed-effects model fit by maximum likelihood
##  Data: subD 
##        AIC      BIC    logLik
##   2135.608 2151.228 -1062.804
## 
## Random effects:
##  Formula: ~1 | ppn
##         (Intercept)
## StdDev: 0.008885091
## 
##  Formula: ~1 | target %in% ppn
##         (Intercept) Residual
## StdDev:    135.2493 2.397586
## 
## Fixed effects: asynchrony_L2L1 ~ stress 
##                      Value Std.Error  DF   t-value p-value
## (Intercept)      27.095238  14.84788 165 1.8248560  0.0698
## stressdifference  2.428571  20.99807 165 0.1156569  0.9081
##  Correlation: 
##                  (Intr)
## stressdifference -0.707
## 
## Standardized Within-Group Residuals:
##          Min           Q1          Med           Q3          Max 
## -0.085892616 -0.007144199 -0.002239972  0.007750989  0.057509243 
## 
## Number of Observations: 168
## Number of Groups: 
##             ppn target %in% ppn 
##               2             168

Power analysis

https://link.springer.com/article/10.3758/s13428-021-01546-0

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.